How AI Engineers Actually Use Datasets: Test Cases, Edge Cases

Based on the provided information, it seems like we are discussing how to evaluate and build an incident response agent or system using structured test cases. Here's a summary of key points and steps you can follow to create your own test suite for evaluating such systems:

Key Components of Test Cases

1. Scenario Description: A brief description of the scenario, including the expected root cause.
2. Inputs:
   • Logs: Relevant log entries that provide context about the incident.
   • Metrics: Time-series data showing trends and thresholds relevant to the scenario.
   • Alerts: The initial alert or signal that triggers investigation.
3. Expected Root Cause: What is the actual cause of the problem?
4. Red Herrings: Misleading signals or symptoms that might lead an agent astray if it only pattern matches.
5. Expected Steps: A list of steps you expect a reasoning agent to take in order to diagnose and resolve the issue.
6. Incorrect Path: A path that a pattern-matching system might follow, which leads to incorrect recommendations.

Example Test Case Structure

Here's an example based on your provided adversarial test case:

python
 
1ADVERSARIAL_TEST = {
2
3[Read the full article at DEV Community](https://dev.to/kalio/how-ai-engineers-actually-use-datasets-test-cases-edge-cases-35gf)
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